This invention relates to control methods and apparatuses, and in particular to electronic or computerised control apparatuses incorporating predictive electronic filters.
1. Field
Many types of control system are known and implemented in all manner of consumer and industrial machines and in industrial processes. These control systems are invariably based on a closed loop in which a control variable is sensed, compared with a desired or set value to derive an error signal, and a correlation applied in response to the error signal, hopefully to drive the error to zero. The stability of the control system is paramount, however, and filter functions are included in the feedback paths implicitly or explicitly to maintain stability. Over the years, many additional feedback and “feed-forward” mechanisms have evolved to improve the performance of control systems, particularly in their speed of response. However, the need for stability limits these approaches in a well-known manner, particularly when filter functions have to be calculated and implemented allowing for variations and tolerances in a range of conditions.
2. Brief Description of Related Art
It has been recognised that many control systems are influenced by measurable “disturbance” factors in the environment, which can be measured independently of the main control variable. Some prior attempts have been made to include the compensation of disturbance signals within the control system. These are limited in their applicability, however, because they rely on analytical or empirical models of the process being controlled, which are not always available, or, more importantly, are not stable between samples, or over time. It would be desirable to provide a control system which can observe and learn the correlation between various signals, and learn automatically to control the apparatus better as a result.
The goal of all electronic filters is to separate the desired signal from all other signal components, called noise. Predictive adaptive filters are also known and exploit the fact that a signal is usually changing slowly compared to the change of the additive noise. This is due to noise comprising all frequencies, while signals comprise predominantly low frequencies. Initially such an adaptive filter has a pre-defined setting but this setting will then continuously adapt to the changing signal, seeking to eliminate the noise in an optimal manner. This is achieved by comparing the momentarily arriving signal to the signal of the immediate past by means of an intrinsically built in mechanism, which can be pictured as an auto-correlation. Such filters react to changes in the signal to adapt the filter settings accordingly. As a consequence such filters are able to predict (extrapolate) the shape of the input signal for the immediate future, with decreasing prediction reliability for increasing temporal (predictive) intervals.
One class of adaptive predictive filter is the Kalman-type filters, which are able to adapt to the characteristics of an input signal using recursive estimation. By such an adaptation mechanism these filters remain optimally tuned to their respective task. Due to the extent to which analogue electronic signals are disturbed by noise, such predictive adaptive filters have a broad application domain in all fields of analogue electronic signal processing, for example, telecommunication, broadcasting, radar signal processing and many more. These filters, however, rely on the self-similarity of the input signal (that is, they are optimised to respond to particular characteristics of the expected signal). Their aim is to preserve a maximum amount of information from the input signal. Thus, the output signal is usually only an improved version of the original signal derived directly from the input. Accordingly, the Kalman filter, although interesting in itself, does not offer a solution to the problems of complex, control systems.